{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "基于data.csv数据，建立mlp模型，计算其在测试数据上的准确率，可视化模型预测结果：\n",
    "\n",
    "1.进行数据分离：test_size=0.33,random_state=10\n",
    "2.模型结构：一层隐藏层，有20个神经元"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "@Author  : Flare Zhao\n",
    "@Email: 454209979@qq.com\n",
    "@QQ讨论群：530533630  申请加群的验证信息为订单号（粘贴号码数字即可）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0323</td>\n",
       "      <td>0.0244</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0887</td>\n",
       "      <td>0.0244</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.1690</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2420</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2420</td>\n",
       "      <td>0.0488</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       x1      x2  y\n",
       "0  0.0323  0.0244  1\n",
       "1  0.0887  0.0244  1\n",
       "2  0.1690  0.0163  1\n",
       "3  0.2420  0.0000  1\n",
       "4  0.2420  0.0488  1"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loada the data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0323</td>\n",
       "      <td>0.0244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0887</td>\n",
       "      <td>0.0244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.1690</td>\n",
       "      <td>0.0163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2420</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2420</td>\n",
       "      <td>0.0488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       x1      x2\n",
       "0  0.0323  0.0244\n",
       "1  0.0887  0.0244\n",
       "2  0.1690  0.0163\n",
       "3  0.2420  0.0000\n",
       "4  0.2420  0.0488"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#define the X and y\n",
    "X = data.drop(['y'],axis=1)\n",
    "y = data.loc[:,'y']\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize the data\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "fig1 = plt.figure(figsize=(5,5))\n",
    "passed=plt.scatter(X.loc[:,'x1'][y==1],X.loc[:,'x2'][y==1])\n",
    "failed=plt.scatter(X.loc[:,'x1'][y==0],X.loc[:,'x2'][y==0])\n",
    "plt.legend((passed,failed),('passed','failed'))\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('x2')\n",
    "plt.title('raw data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(275, 2) (136, 2) (411, 2)\n"
     ]
    }
   ],
   "source": [
    "#split the data\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.33,random_state=10)\n",
    "print(X_train.shape,X_test.shape,X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 20)                60        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 81\n",
      "Trainable params: 81\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#set up the model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "\n",
    "mlp = Sequential()\n",
    "mlp.add(Dense(units=20, input_dim=2, activation='sigmoid'))\n",
    "mlp.add(Dense(units=1,activation='sigmoid'))\n",
    "mlp.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#compile the model\n",
    "mlp.compile(optimizer='adam',loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3000\n",
      "275/275 [==============================] - 0s 912us/step - loss: 1.1788\n",
      "Epoch 2/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 1.1297\n",
      "Epoch 3/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 1.0842\n",
      "Epoch 4/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 1.0406\n",
      "Epoch 5/3000\n",
      "275/275 [==============================] - 0s 49us/step - loss: 1.0004\n",
      "Epoch 6/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.9637\n",
      "Epoch 7/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.9293\n",
      "Epoch 8/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.8963\n",
      "Epoch 9/3000\n",
      "275/275 [==============================] - 0s 47us/step - loss: 0.8694\n",
      "Epoch 10/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.8441\n",
      "Epoch 11/3000\n",
      "275/275 [==============================] - 0s 53us/step - loss: 0.8209\n",
      "Epoch 12/3000\n",
      "275/275 [==============================] - 0s 47us/step - loss: 0.8021\n",
      "Epoch 13/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.7847\n",
      "Epoch 14/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.7698\n",
      "Epoch 15/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.7572\n",
      "Epoch 16/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.7455\n",
      "Epoch 17/3000\n",
      "275/275 [==============================] - 0s 62us/step - loss: 0.7372\n",
      "Epoch 18/3000\n",
      "275/275 [==============================] - 0s 62us/step - loss: 0.7295\n",
      "Epoch 19/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.7219\n",
      "Epoch 20/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.7166\n",
      "Epoch 21/3000\n",
      "275/275 [==============================] - 0s 47us/step - loss: 0.7128\n",
      "Epoch 22/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.7088\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     "text": [
      "Epoch 2261/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 2449/3000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "275/275 [==============================] - 0s 62us/step - loss: 0.1801\n",
      "Epoch 2868/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.1800\n",
      "Epoch 2869/3000\n",
      "275/275 [==============================] - 0s 62us/step - loss: 0.1798\n",
      "Epoch 2870/3000\n",
      "275/275 [==============================] - 0s 62us/step - loss: 0.1800\n",
      "Epoch 2871/3000\n",
      "275/275 [==============================] - 0s 65us/step - loss: 0.1795\n",
      "Epoch 2872/3000\n",
      "275/275 [==============================] - 0s 62us/step - loss: 0.1794\n",
      "Epoch 2873/3000\n",
      "275/275 [==============================] - 0s 65us/step - loss: 0.1792\n",
      "Epoch 2874/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.1791\n",
      "Epoch 2875/3000\n",
      "275/275 [==============================] - 0s 62us/step - loss: 0.1789\n",
      "Epoch 2876/3000\n",
      "275/275 [==============================] - 0s 69us/step - loss: 0.1790\n",
      "Epoch 2877/3000\n",
      "275/275 [==============================] - 0s 65us/step - loss: 0.1788\n",
      "Epoch 2878/3000\n",
      "275/275 [==============================] - ETA: 0s - loss: 0.211 - 0s 65us/step - loss: 0.1785\n",
      "Epoch 2879/3000\n",
      "275/275 [==============================] - 0s 69us/step - loss: 0.1784\n",
      "Epoch 2880/3000\n",
      "275/275 [==============================] - 0s 65us/step - loss: 0.1783\n",
      "Epoch 2881/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1781\n",
      "Epoch 2882/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.1780\n",
      "Epoch 2883/3000\n",
      "275/275 [==============================] - 0s 69us/step - loss: 0.1779\n",
      "Epoch 2884/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1777\n",
      "Epoch 2885/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1776\n",
      "Epoch 2886/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1775\n",
      "Epoch 2887/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.1774\n",
      "Epoch 2888/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.1773\n",
      "Epoch 2889/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1772\n",
      "Epoch 2890/3000\n",
      "275/275 [==============================] - 0s 65us/step - loss: 0.1769\n",
      "Epoch 2891/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.1769\n",
      "Epoch 2892/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1767\n",
      "Epoch 2893/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1767\n",
      "Epoch 2894/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.1764\n",
      "Epoch 2895/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1763\n",
      "Epoch 2896/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1763\n",
      "Epoch 2897/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1763\n",
      "Epoch 2898/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1758\n",
      "Epoch 2899/3000\n",
      "275/275 [==============================] - 0s 52us/step - loss: 0.1761\n",
      "Epoch 2900/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1758\n",
      "Epoch 2901/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1756\n",
      "Epoch 2902/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1756\n",
      "Epoch 2903/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1753\n",
      "Epoch 2904/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.1752\n",
      "Epoch 2905/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1752\n",
      "Epoch 2906/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1748\n",
      "Epoch 2907/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.1748\n",
      "Epoch 2908/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1746\n",
      "Epoch 2909/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1745\n",
      "Epoch 2910/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1744\n",
      "Epoch 2911/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1742\n",
      "Epoch 2912/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1741\n",
      "Epoch 2913/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1740\n",
      "Epoch 2914/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1738\n",
      "Epoch 2915/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1738\n",
      "Epoch 2916/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1736\n",
      "Epoch 2917/3000\n",
      "275/275 [==============================] - 0s 52us/step - loss: 0.1736\n",
      "Epoch 2918/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1734\n",
      "Epoch 2919/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1732\n",
      "Epoch 2920/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1731\n",
      "Epoch 2921/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1729\n",
      "Epoch 2922/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1729\n",
      "Epoch 2923/3000\n",
      "275/275 [==============================] - 0s 47us/step - loss: 0.1726\n",
      "Epoch 2924/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1725\n",
      "Epoch 2925/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1724\n",
      "Epoch 2926/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1724\n",
      "Epoch 2927/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1724\n",
      "Epoch 2928/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1720\n",
      "Epoch 2929/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1720\n",
      "Epoch 2930/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1718\n",
      "Epoch 2931/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1716\n",
      "Epoch 2932/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1716\n",
      "Epoch 2933/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.1716\n",
      "Epoch 2934/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1713\n",
      "Epoch 2935/3000\n",
      "275/275 [==============================] - 0s 65us/step - loss: 0.1713\n",
      "Epoch 2936/3000\n",
      "275/275 [==============================] - 0s 76us/step - loss: 0.1710\n",
      "Epoch 2937/3000\n",
      "275/275 [==============================] - 0s 62us/step - loss: 0.1710\n",
      "Epoch 2938/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.1708\n",
      "Epoch 2939/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1707\n",
      "Epoch 2940/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1705\n",
      "Epoch 2941/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1705\n",
      "Epoch 2942/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1703\n",
      "Epoch 2943/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1702\n",
      "Epoch 2944/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1701\n",
      "Epoch 2945/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1700\n",
      "Epoch 2946/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1699\n",
      "Epoch 2947/3000\n",
      "275/275 [==============================] - 0s 62us/step - loss: 0.1698\n",
      "Epoch 2948/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1695\n",
      "Epoch 2949/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1695\n",
      "Epoch 2950/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.1693\n",
      "Epoch 2951/3000\n",
      "275/275 [==============================] - 0s 62us/step - loss: 0.1693\n",
      "Epoch 2952/3000\n",
      "275/275 [==============================] - 0s 62us/step - loss: 0.1691\n",
      "Epoch 2953/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1689\n",
      "Epoch 2954/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1688\n",
      "Epoch 2955/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1687\n",
      "Epoch 2956/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1686\n",
      "Epoch 2957/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1685\n",
      "Epoch 2958/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1683\n",
      "Epoch 2959/3000\n",
      "275/275 [==============================] - 0s 56us/step - loss: 0.1690\n",
      "Epoch 2960/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.1679\n",
      "Epoch 2961/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1679\n",
      "Epoch 2962/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1679\n",
      "Epoch 2963/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1678\n",
      "Epoch 2964/3000\n",
      "275/275 [==============================] - 0s 62us/step - loss: 0.1677\n",
      "Epoch 2965/3000\n",
      "275/275 [==============================] - 0s 56us/step - loss: 0.1676\n",
      "Epoch 2966/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1674\n",
      "Epoch 2967/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1673\n",
      "Epoch 2968/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1671\n",
      "Epoch 2969/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1670\n",
      "Epoch 2970/3000\n",
      "275/275 [==============================] - 0s 47us/step - loss: 0.1669\n",
      "Epoch 2971/3000\n",
      "275/275 [==============================] - 0s 47us/step - loss: 0.1668\n",
      "Epoch 2972/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1666\n",
      "Epoch 2973/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1666\n",
      "Epoch 2974/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1664\n",
      "Epoch 2975/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1665\n",
      "Epoch 2976/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1663\n",
      "Epoch 2977/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1660\n",
      "Epoch 2978/3000\n",
      "275/275 [==============================] - 0s 48us/step - loss: 0.1659\n",
      "Epoch 2979/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.1661\n",
      "Epoch 2980/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1657\n",
      "Epoch 2981/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1655\n",
      "Epoch 2982/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1654\n",
      "Epoch 2983/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1655\n",
      "Epoch 2984/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1653\n",
      "Epoch 2985/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1651\n",
      "Epoch 2986/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1651\n",
      "Epoch 2987/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1650\n",
      "Epoch 2988/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.1647\n",
      "Epoch 2989/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1645\n",
      "Epoch 2990/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1645\n",
      "Epoch 2991/3000\n",
      "275/275 [==============================] - ETA: 0s - loss: 0.174 - 0s 55us/step - loss: 0.1643\n",
      "Epoch 2992/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1642\n",
      "Epoch 2993/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1643\n",
      "Epoch 2994/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1639\n",
      "Epoch 2995/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1640\n",
      "Epoch 2996/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1639\n",
      "Epoch 2997/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1637\n",
      "Epoch 2998/3000\n",
      "275/275 [==============================] - 0s 55us/step - loss: 0.1635\n",
      "Epoch 2999/3000\n",
      "275/275 [==============================] - 0s 58us/step - loss: 0.1634\n",
      "Epoch 3000/3000\n",
      "275/275 [==============================] - 0s 51us/step - loss: 0.1634\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x21531a35cf8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train the model\n",
    "mlp.fit(X_train,y_train,epochs=3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9745454545454545\n"
     ]
    }
   ],
   "source": [
    "#make prediction and calculate the accuracy\n",
    "y_train_predict = mlp.predict_classes(X_train)\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_train = accuracy_score(y_train,y_train_predict)\n",
    "print(accuracy_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9191176470588235\n"
     ]
    }
   ],
   "source": [
    "#make prediction based on the test data\n",
    "y_test_predict = mlp.predict_classes(X_test)\n",
    "accuracy_test = accuracy_score(y_test,y_test_predict)\n",
    "print(accuracy_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]]\n"
     ]
    }
   ],
   "source": [
    "print(y_train_predict[0:10])\n",
    "#y_train_predict_form = pd.Series(i[0] for i in y_train_predict)\n",
    "\n",
    "#print(y_train_predict_form)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "#generate new data for plot\n",
    "xx, yy = np.meshgrid(np.arange(0,1,0.01),np.arange(0,1,0.01))\n",
    "x_range = np.c_[xx.ravel(),yy.ravel()]\n",
    "y_range_predict = mlp.predict_classes(x_range)\n",
    "print(type(y_range_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       1\n",
      "1       1\n",
      "2       1\n",
      "3       1\n",
      "4       1\n",
      "       ..\n",
      "9995    1\n",
      "9996    1\n",
      "9997    1\n",
      "9998    1\n",
      "9999    1\n",
      "Length: 10000, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#format the output\n",
    "y_range_predict_form = pd.Series(i[0] for i in y_range_predict)\n",
    "\n",
    "print(y_range_predict_form)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure(figsize=(5,5))\n",
    "passed_predict=plt.scatter(x_range[:,0][y_range_predict_form==1],x_range[:,1][y_range_predict_form==1])\n",
    "failed_predict=plt.scatter(x_range[:,0][y_range_predict_form==0],x_range[:,1][y_range_predict_form==0])\n",
    "\n",
    "passed=plt.scatter(X.loc[:,'x1'][y==1],X.loc[:,'x2'][y==1])\n",
    "failed=plt.scatter(X.loc[:,'x1'][y==0],X.loc[:,'x2'][y==0])\n",
    "plt.legend((passed,failed,passed_predict,failed_predict),('passed','failed','passed_predict','failed_predict'))\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('x2')\n",
    "plt.title('prediction result')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "好坏质检二分类mlp实战summary：\n",
    "1、通过mlp模型，在不增加特征项的情况下，实现了非线性二分类任务；\n",
    "2、掌握了mlp模型的建立、配置与训练方法，并实现基于新数据的预测；\n",
    "3、熟悉了mlp分类的预测数据格式，并实现格式转换；\n",
    "4、核心算法参考链接：https://keras-cn.readthedocs.io/en/latest/#30skeras"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
